基于时间频率卷积相互作用的增量转移学习,用于多任务预测风速和风力功率.
Ke Fu1, Bowen Yuan2, Baihui An2
1Institute for Ocean Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
概括
本研究引入了一个增量转移学习框架,用于准确预测风速和功率. 这种新型模型提高了预测准确度,特别是对于数据有限的新风电场.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 风速和功率预测对于风能至关重要,但由于数据异质性和时间变化的特性而受到挑战.
- 由于有限的历史数据和数据机密性问题,现有模型与新风电场作斗争.
研究的目的:
- 开发一个准确和高效的多任务预测风速和功率模型.
- 通过增量转移学习解决新风电场有限历史数据的挑战.
主要方法:
- 提出了一个增量转移学习框架,用于同时预测风速和功率.
- 开发了一个时间频率卷积交互神经网络,集成了循环卷积和一个封闭的循环单元.
- 该模型通过新数据来改进历史预测,以捕捉不断变化的统计特征.
主要成果:
- 拟议的模型在来自中国北部的真实数据上实现了最高的预测准确性.
- 增量转移学习方法在较长时间内显示出更好的预测性能.
- 该模型有效地捕捉了有限样本的动态行为,减少了计算要求.
结论:
- 开发的模型显示了风能实际工程应用的巨大潜力.
- 增量转移学习在改善不断变化的风数据模式的预测准确性方面是有效的.
- 该研究突出了一个可行的解决方案,以有限的数据准确地预测风力发电.
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